As is known in the art, it would be desirable to have combustion noise of diesel engines to be on the same level as or less than that of gasoline engines. To achieve this, the noise is measured during development when the engine control strategy is calibrated. Noise can be measured with microphones in anechoic chambers; but, these chambers are rather expensive. Hence, during development, noise is usually assessed in a different, cheaper way. Combustion noise of internal combustion engines can be derived by use of a device for measuring the pressure in the cylinder. Such noise assessment, in the form of a noise index, may be obtained by filtering an “in-cylinder” pressure sensor signal and then taking its root mean square (RMS) value. An additional advantage of cylinder pressure derived noise assessment is that measurements can also be collected in a vehicle on the road or on a dynamometer test stand.
As is also known in the art, a method for determining noise levels of an internal combustion engine is described in European Patent Application EP1209458 assigned to the same assignee as the present invention. Such patent application describes the use of a shift variant wavelet transform. Wavelet transforms can be compared to a Fourier transform. As for the latter, the signal's frequency content is revealed. However, wavelet transforms are more powerful because they allow localizing signal properties both in time (or space) and frequency at the same time. For the noise index application, two properties of the wavelet transform are utilized: Firstly, the wavelet transform is equivalent to bandpass filtering. By retaining only certain levels of the wavelet transform, the frequency content of the transformed signal can be chosen. Secondly, the wavelet transform with orthogonal wavelets preserves the energy of the signal (Parseval theorem). Thus, the RMS value can be computed from the transformed signal rather than the filtered original signal. The advantage of the wavelet approach to noise index computation over the traditional filtering approach is that it is computationally less intensive (depending on the order of the wavelets) and that additional information for other applications can be extracted from the wavelet transformed signal at the same time.
More particularly, referring to FIG. 1, the time-continuous waveform is fed to a sampler with such samples to produce the approximate signal, λ0. The approximate signal, λ0, is fed to a one-step wavelet transformer. The one step wavelet transformer (1 step wvlt tf) includes a high pass filter (HPF) and a low pass filter (LPF) for, after downsampling the produced data samples by a factor of 2, produce an new approximate signal, λ1, from the LPF and a first detail signal or coefficient, γ1, from the HPF, as shown. The process repeats feeding into the next succeeding wavelet transformer the preceding approximate signal.
Thus, a wavelet transform decomposes a signal into one approximation signal and several detail signals. The transform is done by filtering and down-sampling the original signal repeatedly, which leads to detail signals with band limited frequency contents. The FIR filters applied for the wavelet transform are the lowpass and highpass filters. The detail signal's frequency content is given by the frequency bands of the highpass filter; whereas, the frequency content of the approximation signal is limited by the frequency band of the lowpass filter. A Parseval theorem relates the signal transformed with orthogonal wavelets to the signal energy in a window containing n samples:       ∫                                                  f            ⁡                          (              x              )                                                2            ⁢                           ⁢              ⅆ        x              =                    ∑                  l          =          1                          n          /                      2            N                              ⁢                                              λ                          N              ,              l                                                2              +                  ∑                  j          =          1                N            ⁢                        ∑                      k            =            1                                n            /                          2              l                                      ⁢                                                        γ                              j                ,                k                                                          2                    where N is the number of wavelet transform steps, j is an index running over all these steps, and k and l are indices running over all the elements in the respective signals.
For the wavelet-based noise meter, only the detail signals of those levels, or wavelet steps are retained whose frequency bands cover the frequency range of interest, i.e., the frequency band of a traditional noise meter. Once the right levels, jm, of the wavelet transform have been chosen, the wavelet based noise index, NIWVL, is defined as:                               NI          WVL                =                  20          ·                                    log              10                        ⁡                          (                                                c                  n                                ·                                                                            ∑                                              j                        m                                                              ⁢                                                                  ∑                        k                                            ⁢                                                                                                                              γ                                                                                          j                                m                                                            ,                              k                                                                                                                                2                                                                                                        )                                                          Equation  (1)            where c is a scaling factor to adjust the absolute noise level, n is the number of samples of the original signal, and k is the number of the n samples being used in the computation of equation (1), where k runs from 1 to n and is therefore referred to herein as a “running index”.
Thus, a batch of n samples of the in-cylinder pressure signal is taken for each cylinder per engine cycle. Each one of the samples in the batch of the n samples is designated by an index k, where k runs from 1 to n. For each batch, one noise index NIWVL is calculated in accordance with equation (1) above. Thus, for each crankshaft revolution of a four-cylinder engine, there are two batches with n samples each, and a noise index NIWVL is calculated every 180 degrees crank angle.